276 lines
6.8 KiB
Python
276 lines
6.8 KiB
Python
from logging import getLogger
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from typing import Any, List
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import numpy as np
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import torch
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from diffusers import OnnxStableDiffusionImg2ImgPipeline, OnnxStableDiffusionPipeline
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from PIL import Image
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from onnx_web.chain import blend_mask
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from onnx_web.chain.base import ChainProgress
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from ..chain import upscale_outpaint
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from ..output import save_image, save_params
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from ..params import Border, ImageParams, Size, StageParams
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from ..server.device_pool import JobContext
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from ..server.upscale import UpscaleParams, run_upscale_correction
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from ..utils import ServerContext, run_gc
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from .load import get_latents_from_seed, load_pipeline
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logger = getLogger(__name__)
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def run_txt2img_pipeline(
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job: JobContext,
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server: ServerContext,
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params: ImageParams,
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size: Size,
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output: str,
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upscale: UpscaleParams,
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) -> None:
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latents = get_latents_from_seed(params.seed, size)
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pipe = load_pipeline(
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server,
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OnnxStableDiffusionPipeline,
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params.model,
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params.scheduler,
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job.get_device(),
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params.lpw,
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)
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progress = job.get_progress_callback()
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if params.lpw:
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logger.debug("using LPW pipeline for txt2img")
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rng = torch.manual_seed(params.seed)
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result = pipe.text2img(
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params.prompt,
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height=size.height,
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width=size.width,
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generator=rng,
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guidance_scale=params.cfg,
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latents=latents,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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callback=progress,
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)
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else:
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rng = np.random.RandomState(params.seed)
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result = pipe(
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params.prompt,
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height=size.height,
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width=size.width,
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generator=rng,
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guidance_scale=params.cfg,
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latents=latents,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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callback=progress,
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)
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image = result.images[0]
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image = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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image,
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upscale=upscale,
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callback=progress,
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)
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dest = save_image(server, output, image)
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save_params(server, output, params, size, upscale=upscale)
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del pipe
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del image
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del result
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run_gc([job.get_device()])
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logger.info("finished txt2img job: %s", dest)
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def run_img2img_pipeline(
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job: JobContext,
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server: ServerContext,
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params: ImageParams,
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output: str,
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upscale: UpscaleParams,
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source_image: Image.Image,
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strength: float,
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) -> None:
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pipe = load_pipeline(
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server,
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OnnxStableDiffusionImg2ImgPipeline,
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params.model,
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params.scheduler,
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job.get_device(),
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params.lpw,
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)
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progress = job.get_progress_callback()
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if params.lpw:
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logger.debug("using LPW pipeline for img2img")
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rng = torch.manual_seed(params.seed)
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result = pipe.img2img(
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source_image,
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params.prompt,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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strength=strength,
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callback=progress,
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)
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else:
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rng = np.random.RandomState(params.seed)
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result = pipe(
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params.prompt,
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source_image,
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generator=rng,
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guidance_scale=params.cfg,
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negative_prompt=params.negative_prompt,
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num_inference_steps=params.steps,
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strength=strength,
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callback=progress,
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)
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image = result.images[0]
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image = run_upscale_correction(
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job,
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server,
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StageParams(),
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params,
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image,
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upscale=upscale,
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callback=progress,
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)
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dest = save_image(server, output, image)
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size = Size(*source_image.size)
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save_params(server, output, params, size, upscale=upscale)
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del pipe
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del image
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del result
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run_gc([job.get_device()])
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logger.info("finished img2img job: %s", dest)
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def run_inpaint_pipeline(
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job: JobContext,
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server: ServerContext,
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params: ImageParams,
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size: Size,
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output: str,
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upscale: UpscaleParams,
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source_image: Image.Image,
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mask_image: Image.Image,
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border: Border,
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noise_source: Any,
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mask_filter: Any,
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strength: float,
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fill_color: str,
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tile_order: str,
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) -> None:
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# device = job.get_device()
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progress = job.get_progress_callback()
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stage = StageParams(tile_order=tile_order)
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# calling the upscale_outpaint stage directly needs accumulating progress
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progress = ChainProgress.from_progress(progress)
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logger.debug("applying mask filter and generating noise source")
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image = upscale_outpaint(
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job,
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server,
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stage,
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params,
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source_image,
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border=border,
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mask_image=mask_image,
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fill_color=fill_color,
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mask_filter=mask_filter,
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noise_source=noise_source,
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callback=progress,
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)
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image = run_upscale_correction(
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job, server, stage, params, image, upscale=upscale, callback=progress
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)
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dest = save_image(server, output, image)
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save_params(server, output, params, size, upscale=upscale, border=border)
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del image
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run_gc([job.get_device()])
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logger.info("finished inpaint job: %s", dest)
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def run_upscale_pipeline(
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job: JobContext,
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server: ServerContext,
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params: ImageParams,
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size: Size,
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output: str,
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upscale: UpscaleParams,
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source_image: Image.Image,
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) -> None:
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# device = job.get_device()
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progress = job.get_progress_callback()
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stage = StageParams()
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image = run_upscale_correction(
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job, server, stage, params, source_image, upscale=upscale, callback=progress
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)
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dest = save_image(server, output, image)
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save_params(server, output, params, size, upscale=upscale)
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del image
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run_gc([job.get_device()])
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logger.info("finished upscale job: %s", dest)
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def run_blend_pipeline(
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job: JobContext,
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server: ServerContext,
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params: ImageParams,
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size: Size,
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output: str,
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upscale: UpscaleParams,
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sources: List[Image.Image],
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mask: Image.Image,
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) -> None:
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progress = job.get_progress_callback()
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stage = StageParams()
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image = blend_mask(
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job,
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server,
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stage,
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params,
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resized=sources,
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mask=mask,
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callback=progress,
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)
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image = image.convert("RGB")
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image = run_upscale_correction(
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job, server, stage, params, image, upscale=upscale, callback=progress
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)
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dest = save_image(server, output, image)
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save_params(server, output, params, size, upscale=upscale)
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del image
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run_gc([job.get_device()])
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logger.info("finished blend job: %s", dest)
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